Comment by maurits

The most popular choices seem to be:

Machine Learning: a Probabilistic Perspective, by Murphy

http://www.cs.ubc.ca/~murphyk/MLbook/

Pattern classification, by Duda et all

http://www.amazon.com/Pattern-Classification-Pt-1-Richard-Du...

The Elements of Statistical Learning, by Hastie et all. It is free from Stanford.

http://www-stat.stanford.edu/~tibs/ElemStatLearn

Mining of Massive Datasets, free from Stanford.

http://infolab.stanford.edu/~ullman/mmds.html

Bayesian Reasoning and Machine Learning, by Barber, free available online.

http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...

Learning from data, by Abu-Mostafa.

It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html

Pattern Recognition and Machine Learning, by Bischop

http://research.microsoft.com/en-us/um/people/cmbishop/prml/

Also noteworthy

Information Theory, Inference, and Learning Algorithms, by Mackay, free.

http://www.inference.phy.cam.ac.uk/itprnn/book.html

Classification, Parameter Estimation and State Estimation, by van der Heijden.

http://prtools.org

Computer Vision: Models, Learning, and Inference, by Prince, available for free

http://www.computervisionmodels.com/

Probabilistic Graphical Models, by Koller. Has an accompanying course on Coursera.